Integrated Dynamic Evaluation and Prospect Screening for Infill-Well Optimization in a Tight Sandstone Gas Reservoir

As a typical tight sandstone gas reservoir, the study area has entered a production-decline stage after years of development, and well pattern infilling has become a key means of improving the recovery factor. A cluster-analysis-based classification divided the reservoir into three types (Type I: 21%, Type II: 33%, Type III: 46%), and rate-transient analysis across the full well population (446 wells, 86.3% analysis success rate) yielded a total dynamic reserve of 20.13 billion cubic meters and an EUR of 18.408 billion cubic meters for the study area. Based on these results, a quantitative multicriteria decision model was developed to screen infill potential areas, weighting reservoir type, energy storage coefficient, well-control coverage, formation pressure, and remaining-reserve enrichment. Four infill potential well pads (0114, 0075, 0134, and 0146) were identified, with 11 infill wells deployed, predicting an EUR increase of 300 million cubic meters. The results provide a quantitative, reproducible basis for infill-well deployment in the study area and in analogous tight gas reservoirs.

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Publication Details

Journal
Energies
Published
2026-09-09
DOI
https://doi.org/10.3390/en19184270
Primary Topic
Hydraulic Fracturing and Reservoir Analysis
Type
article
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Integrated Dynamic Evaluation and Prospect Screening for Infill-Well Optimization in a Tight Sandstone Gas Reservoir

Xiaofeng Li, Bo Wang, Penghui Zhang, Jiaen Lin et al.
Energies
Hydraulic Fracturing and Reservoir Analysis
article

Integrated Dynamic Evaluation and Prospect Screening for Infill-Well Optimization in a Tight Sandstone Gas Reservoir

Xiaofeng Li, Bo Wang, Penghui Zhang, Jiaen Lin, Rongjun Zhang, Liming Guo, Ziyuan Meng, Xinyu Zhong, Liang Xu
article en

Abstract

As a typical tight sandstone gas reservoir, the study area has entered a production-decline stage after years of development, and well pattern infilling has become a key means of improving the recovery factor. A cluster-analysis-based classification divided the reservoir into three types (Type I: 21%, Type II: 33%, Type III: 46%), and rate-transient analysis across the full well population (446 wells, 86.3% analysis success rate) yielded a total dynamic reserve of 20.13 billion cubic meters and an EUR of 18.408 billion cubic meters for the study area. Based on these results, a quantitative multicriteria decision model was developed to screen infill potential areas, weighting reservoir type, energy storage coefficient, well-control coverage, formation pressure, and remaining-reserve enrichment. Four infill potential well pads (0114, 0075, 0134, and 0146) were identified, with 11 infill wells deployed, predicting an EUR increase of 300 million cubic meters. The results provide a quantitative, reproducible basis for infill-well deployment in the study area and in analogous tight gas reservoirs.

EnergiesVol. 19(18)
Xi'an Shiyou University (CN), Daqing Oilfield General Hospital (CN), University of Petroleum (ID), Oil and Gas Center (CN)
Openalex Percentile: Top 20%
Hydraulic Fracturing and Reservoir Analysis
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Integrated Dynamic Evaluation and Prospect Screening for Infill-Well Optimization in a Tight Sandstone Gas Reservoir — Xiaofeng Li, Bo Wang, et al. · Energies (2026) | TGRS Research Map | TGRS